Lesbian, gay, bisexual, and transgender (LGBT) physicians' experiences in the workplace

Little is known about the experiences of lesbian, gay, bisexual, and transgender (LGBT) physicians in the workplace. There is little formal education in medical school about LGBT issues, and some heterosexual physicians have negative attitudes about caring for LGBT patients or working with LGBT coworkers, setting the stage for an exclusive and unwelcoming workplace. The current study used an online survey to assess a convenience sample of 427 LGBT physicians from a database of a national LGBT healthcare organization, as well as a snowball sample generated from the members of the database. Although rates of discriminatory behaviors had decreased since earlier reports, 10% reported that they were denied referrals from heterosexual colleagues, 15% had been harassed by a colleague, 22% had been socially ostracized, 65% had heard derogatory comments about LGBT individuals, 34% had witnessed discriminatory care of an LGBT patient, 36% had witnessed disrespect toward an LGBT patient's partner, and 27% had witnessed discriminatory treatment of an LGBT coworker. Few had received any formal education on LGBT issues in medical school or residency. It appears that medical schools and health care workplaces continue to ignore LGBT issues and operate in discriminatory fashion far too often.     

Equilibria in Networks

We study a model in which two carriers choose networks to connect cities and compete for customers. We show that if carriers compete aggressively (e.g., Bertrand-like behavior), one carrier operating a single hub-spoke network is an equilibrium outcome. Competing hub-spoke networks are not an equilibrium outcome, although duopoly equilibria in nonhub networks can exist. If carriers do not compete aggressively, an equilibrium with competing hub-spoke networks exists as long as the number of cities is not too small. We provide conditions under which all equilibria consist of hub-spoke networks.     

A content analysis of intervention research in social work doctoral dissertations

It is imperative for the social work profession that the on-going schism between researchers and practitioners commented on in the literature for the past 40 years be effectively addressed. Doctoral programs are in a unique position to affect the production of research focusing on intervention. The purpose of this study was to determine if social work doctoral programs are currently successful in encouraging their doctoral candidates to engage in intervention research. A content analysis was conducted on 252 dissertation abstracts produced by social work doctoral students in 2006. Only 13.49% of the abstracts indicated a focus on social work intervention. The researchers argue that this finding indicates a need for a paradigm shift in social work education and practice.     

Families' experience of a single session therapy framework in advanced allied health practice for children with neurodevelopmental and behavioural disabilities: A thematic analysis of qualitative data

Single session therapy (SST) has emerging evidence for facilitating meaningful therapeutic change in a small number of consults, rather than more traditional long-term therapies. This study aimed to explore and describe the parent/caregiver and child experience with advanced allied health practitioners (AAHP) using SST for children with developmental disabilities, by understanding key characteristics of the consultation that led to therapeutic change. Eligible families attending an AAHP outpatient clinic for children with developmental and behavioural concerns participated in one to four SST consultations around their identified areas of need. Semi-structured interviews were conducted with 38 parents and 12 children, from 37 different families. Thematic analysis was used to analyse interviews. Demographic information was gathered through parent interviews and by reviewing hospital records. Four key themes emerged relating to the characteristics of the consultation that led to a positive experience for families: (1) the art of mutual presence; (2) the value of time; (3) the power of understanding; and (4) the trust of action. These attributes were influenced by the overarching complexities of the AAHP, the parent's and child's interpersonal skills, and the organisational mechanisms influencing patient care. Parents and children reported clinical change during the consult through increased understanding, and being heard, valued, and respected for their individual decisions on how to manage their child's and family's needs. This study provides preliminary evidence that utilising SST for children and families with developmental and behavioural needs facilitates a positive experience valued by families. SST, with a focus on a family's individual identified areas of concern, was valued and effective when delivered by AAHP. This suggests that SST, when appropriately administered by experienced and trained health practitioners, is useful for families dealing with children with developmental and behavioural challenges.     

Spatial Bayesian modeling of GLCM with application to malignant lesion characterization

The emerging field of cancer radiomics endeavors to characterize intrinsic patterns of tumor phenotypes and surrogate markers of response by transforming medical images into objects that yield quantifiable summary statistics to which regression and machine learning algorithms may be applied for statistical interrogation. Recent literature has identified clinicopathological association based on textural features deriving from gray-level co-occurrence matrices (GLCM) which facilitate evaluations of gray-level spatial dependence within a delineated region of interest. GLCM-derived features, however, tend to contribute highly redundant information. Moreover, when reporting selected feature sets, investigators often fail to adjust for multiplicities and commonly fail to convey the predictive power of their findings. This article presents a Bayesian probabilistic modeling framework for the GLCM as a multivariate object as well as describes its application within a cancer detection context based on computed tomography. The methodology, which circumvents processing steps and avoids evaluations of reductive and highly correlated feature sets, uses latent Gaussian Markov random field structure to characterize spatial dependencies among GLCM cells and facilitates classification via predictive probability. Correctly predicting the underlying pathology of 81% of the adrenal lesions in our case study, the proposed method outperformed current practices which achieved a maximum accuracy of only 59%. Simulations and theory are presented to further elucidate this comparison as well as ascertain the utility of applying multivariate Gaussian spatial processes to GLCM objects.     

Correlates of Ethnicity‐related Dating Preferences among Asian American Adolescents across the High School Years

Ethnicity‐related dating preferences among Asian American adolescents and the links between preferences (i.e., for a same‐ethnic dating partner) and ethnic identity centrality and regard, American identity centrality and regard, parent – adolescent closeness, and perceptions of discrimination were investigated. Data from 175 self‐identified Asian American high school students were collected yearly for four consecutive years. Higher levels of ethnic identity centrality and regard and parent – adolescent closeness averaged across four years were associated with preferring a same‐ethnic partner. Moreover, foreign‐born adolescents were less likely to prefer a same‐ethnic partner when they encountered a higher than average level of discrimination on any given year. Results highlight variability in the developmental and individual‐level factors that shape how adolescents navigate their dating relationships.     

Algorithms (and the) everyday

ABSTRACT

Our everyday practices are increasingly mediated through online technologies, entailing the navigation and also oft-simultaneous creation of large quantities of information and communication data. The scale and types of activities being undertaken, the data that are being created and engaged with, and the possibilities for analysis, archiving and distribution are now so extensive that technical constructs are necessarily required as a way to manage, interpret and distribute these. These constructs include the platforms, the software, the codes and the algorithms. This paper explores the place of the algorithm in shaping and engaging with the contemporary everyday. It does this via an exploration of some particular instances of algorithmic sorting and presentation as well as considering some of the ways these contribute to shaping our everyday practices and understandings. In doing so, it raises questions about understandings of agency and power, shifting world views and our complex relationship with technologies.     

Critical values improvement for the standard normal homogeneity test by combining Monte Carlo and regression approaches

The distribution of the test statistics of homogeneity tests is often unknown, requiring the estimation of the critical values through Monte Carlo (MC) simulations. The computation of the critical values at low α, especially when the distribution of the statistics changes with the series length (sample cardinality), requires a considerable number of simulations to achieve a reasonable precision of the estimates (i.e. 10⁶ simulations or more for each series length). If, in addition, the test requires a noteworthy computational effort, the estimation of the critical values may need unacceptably long runtimes.

To overcome the problem, the paper proposes a regression-based refinement of an initial MC estimate of the critical values, also allowing an approximation of the achieved improvement. Moreover, the paper presents an application of the method to two tests: SNHT (standard normal homogeneity test, widely used in climatology), and SNH2T (a version of SNHT showing a squared numerical complexity). For both, the paper reports the critical values for α ranging between 0.1 and 0.0001 (useful for the p-value estimation), and the series length ranging from 10 (widely adopted size in climatological change-point detection literature) to 70,000 elements (nearly the length of a daily data time series 200 years long), estimated with coefficients of variation within 0.22%. For SNHT, a comparison of our results with approximated, theoretically derived, critical values is also performed; we suggest adopting those values for the series exceeding 70,000 elements.     

Tempered stable Ornstein– Uhlenbeck processes: A practical view

We study the one-dimensional Ornstein–Uhlenbeck (OU) processes with marginal law given by tempered stable and tempered infinitely divisible distributions. We investigate the transition law between consecutive observations of these processes and evaluate the characteristic function of integrated tempered OU processes with a view toward practical applications. We then analyze how to draw a random sample from this class of processes by considering both the classical inverse transform algorithm and an acceptance–rejection method based on simulating a stable random sample. Using a maximum likelihood estimation method based on the fast Fourier transform, we empirically assess the simulation algorithm performance.